An Empirical Investigation on the Acceptance of Autonomous Vehicles: Perspective of Drivers’ Self–AV Bias

Abstract

In autonomous vehicles (AVs), especially in fully AVs, “drivers” perceive vehicle operation from a passenger’s perspective. This study focuses on the perspective change of drivers especially in fully AVs. To investigate the effect of change in perspective on drivers’ assessment of AVs, this study conducted a driving simulator experiment and a survey using different samples. The driving simulator experiment was a within-subjects design including 40 participants. The experimental results indicated that although AVs drive exactly the same as the drivers, 85% of these drivers had different assessments for AVs compared to driving themselves (self-AV bias). Among these biased drivers, 80% changed the direction of their assessments (e.g., satisfied with their own driving behaviors but dissatisfied with the same behaviors from the AVs). Additionally, self-AV bias increased with the level of self-evaluation of their own driving skills. The higher the level of self-evaluation, the higher the optimism bias (drivers think they drive better than AVs) of drivers. Thereafter, to show whether and how self-AV bias affects drivers’ intuitive acceptance of AVs, a survey was conducted, which included 381 valid questionnaires. The survey results showed that self-AV bias was a critical factor promoting perceived usefulness of AVs; particularly, highly self-AV-biased drivers responded more to the perceived usefulness of AVs. Moreover, drivers who could accept more risky driving behaviors of AVs compared to themselves also perceived AVs to be more useful. The findings from this study provide insights for understanding drivers’ assessment and acceptance of AVs’ driving behaviors.

Disclosure statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Notes

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Additional information

Funding

This research was supported by the National Natural Science Foundation of China [Grant No. 72222021, 72171168, 72271175, 72010107004].

Notes on contributors

Hongming Dong

Hongming Dong received his PhD from Tianjin University in China. His research is focused on topics related to human factors and ergonomics.

Shoufeng Ma

Shoufeng Ma is a professor in the College of Management and Economics in Tianjin University. His research is focused on topics related to urban traffic management, transportation systems engineering, information management, enterprise informatization and E-commerce, logistic engineering, project management and industrial engineering.

Shuai Ling

Shuai Ling is currently an associate professor in the College of Management and Economics in Tianjin University. His research is focused on topics related to urban traffic management and digital governance.

Geng Li

Geng Li is currently an associate professor in the College of Management and Economics in Tianjin University. His research is focused on urban traffic management and transportation systems engineering.

Shuxian Xu

Shuxian Xu is currently an associate professor in the College of Management and Economics in Tianjin University. Her research is focused on transport economy and urban traffic management.

Bo Song

Bo Song is currently an intermediate engineer in China Automotive Technology and Research Center.